title: A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities.
abstract: As indicated in ICH M7 draft guidance, in silico predictive tools including statistically-based QSARs and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. To address this need, we developed and validated a QSAR model to predict Salmonella t. mutagenicity (Ames assay outcome) of pharmaceutical impurities using Prous Institute's Symmetry(SM), a new in silico solution for drug discovery and toxicity screening, and the Mold2 molecular descriptor package (FDA/NCTR). Data was sourced from public benchmark databases with known Ames assay mutagenicity outcomes for 7300 chemicals (57% mutagens). Of these data, 90% was used to train the model and the remaining 10% was set aside as a holdout set for validation. The model's applicability to drug impurities was tested using a FDA/CDER database of 951 structures, of which 94% were found within the model's applicability domain. The predictive performance of the model is acceptable for supporting regulatory decision-making with 84±1% sensitivity, 81±1% specificity, 83±1% concordance and 79±1% negative predictivity based on internal cross-validation, while the holdout dataset yielded 83% sensitivity, 77% specificity, 80% concordance and 78% negative predictivity. Given the importance of having confidence in negative predictions, an additional external validation of the model was also carried out, using marketed drugs known to be Ames-negative, and obtained 98% coverage and 81% specificity. Additionally, Ames mutagenicity data from FDA/CFSAN was used to create another data set of 1535 chemicals for external validation of the model, yielding 98% coverage, 73% sensitivity, 86% specificity, 81% concordance and 84% negative predictivity.
authors.affiliation: Prous Institute for Biomedical Research, Rambla de Catalunya, 135, 3-2, Barcelona 08008, Spain.
authors.forename: Antoni
authors.initials: A
authors.lastname: Valencia
authors.forename: Josep
authors.initials: J
authors.lastname: Prous
authors.forename: Oscar
authors.initials: O
authors.lastname: Mora
authors.forename: Nakissa
authors.initials: N
authors.lastname: Sadrieh
authors.forename: Luis G
authors.initials: LG
authors.lastname: Valerio
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016208:Databases, Factual; D004340:Drug Contamination; D008956:Models, Chemical; D009152:Mutagenicity Tests; D009153:Mutagens / Q000032:analysis; D021281:Quantitative Structure-Activity Relationship; D018570:Risk Assessment; D012475:Salmonella / Q000235:genetics; D012680:Sensitivity and Specificity; D012984:Software
chemical_list: D009153:Mutagens
keywords: Binned algorithms; Computational toxicology; Modeling; Mutagenicity; QSAR; Safety